File size: 65,772 Bytes
91fc1b6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "OiBSu3YkEcoX"
},
"source": [
"Copyright 2024 DeepMind Technologies Limited.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
"\n",
"http://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Y5OeTiryEcoX"
},
"source": [
"# Fine-tuning the 2B Griffin model with Flax\n",
"\n",
"In this tutorial you will learn how to fine-tune the 2B Griffin model for a simple translation task."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5m81VQOqEcoX"
},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cloning into 'recurrentgemma'...\n",
"remote: Enumerating objects: 52, done.\u001b[K\n",
"remote: Counting objects: 100% (49/49), done.\u001b[K\n",
"remote: Compressing objects: 100% (47/47), done.\u001b[K\n",
"remote: Total 52 (delta 16), reused 5 (delta 2), pack-reused 3\u001b[K\n",
"Receiving objects: 100% (52/52), 74.57 KiB | 1.01 MiB/s, done.\n",
"Resolving deltas: 100% (16/16), done.\n"
]
}
],
"source": [
"!git clone https://github.com/google-deepmind/recurrentgemma.git"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"cellView": "form",
"id": "XpSw-_4EEcoY"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mDEPRECATION: git+https://github.com/google-deepmind/recurrentgemma.git#egg=recurrentgemma[jax] contains an egg fragment with a non-PEP 508 name pip 25.0 will enforce this behaviour change. A possible replacement is to use the req @ url syntax, and remove the egg fragment. Discussion can be found at https://github.com/pypa/pip/issues/11617\u001b[0m\u001b[33m\n",
"\u001b[0mCollecting recurrentgemma[jax]\n",
" Cloning https://github.com/google-deepmind/recurrentgemma.git to /private/var/folders/jx/gld2clwj7sd_q8hd2m6hztcr0000gn/T/pip-install-2c9hrit5/recurrentgemma_54f0084d6e164dc38004db09c24dfacb\n",
" Running command git clone --filter=blob:none --quiet https://github.com/google-deepmind/recurrentgemma.git /private/var/folders/jx/gld2clwj7sd_q8hd2m6hztcr0000gn/T/pip-install-2c9hrit5/recurrentgemma_54f0084d6e164dc38004db09c24dfacb\n",
" Resolved https://github.com/google-deepmind/recurrentgemma.git to commit 0f5ca57442f17c7309c70b0228fd8e5505cbdaa1\n",
" Installing build dependencies ... \u001b[?25ldone\n",
"\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n",
"\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
"\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.21 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from recurrentgemma[jax]) (1.24.4)\n",
"Requirement already satisfied: einops<0.8.0,>=0.7.0 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from recurrentgemma[jax]) (0.7.0)\n",
"Collecting jaxtyping<0.3.0,>=0.2.28\n",
" Downloading jaxtyping-0.2.28-py3-none-any.whl (40 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.7/40.7 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting absl-py<1.5.0,>=1.4.0\n",
" Downloading absl_py-1.4.0-py3-none-any.whl (126 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m126.5/126.5 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting sentencepiece<0.3.0,>=0.2.0\n",
" Downloading sentencepiece-0.2.0-cp310-cp310-macosx_11_0_arm64.whl (1.2 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m27.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n",
"\u001b[?25hCollecting orbax-checkpoint==0.5.7\n",
" Downloading orbax_checkpoint-0.5.7-py3-none-any.whl (159 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m159.2/159.2 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting jax<0.5.0,>=0.4.23\n",
" Downloading jax-0.4.26-py3-none-any.whl (1.9 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m31.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
"\u001b[?25hCollecting flax<0.9.0,>=0.8.2\n",
" Downloading flax-0.8.2-py3-none-any.whl (686 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m686.8/686.8 kB\u001b[0m \u001b[31m43.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting etils[epath,epy]\n",
" Downloading etils-1.7.0-py3-none-any.whl (152 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m152.4/152.4 kB\u001b[0m \u001b[31m18.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: typing_extensions in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from orbax-checkpoint==0.5.7->recurrentgemma[jax]) (4.9.0)\n",
"Requirement already satisfied: pyyaml in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from orbax-checkpoint==0.5.7->recurrentgemma[jax]) (6.0.1)\n",
"Collecting tensorstore>=0.1.51\n",
" Downloading tensorstore-0.1.56-cp310-cp310-macosx_11_0_arm64.whl (13.0 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.0/13.0 MB\u001b[0m \u001b[31m14.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
"\u001b[?25hCollecting msgpack\n",
" Downloading msgpack-1.0.8-cp310-cp310-macosx_11_0_arm64.whl (84 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.9/84.9 kB\u001b[0m \u001b[31m12.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting jaxlib\n",
" Downloading jaxlib-0.4.26-cp310-cp310-macosx_11_0_arm64.whl (66.7 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.7/66.7 MB\u001b[0m \u001b[31m32.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: nest_asyncio in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from orbax-checkpoint==0.5.7->recurrentgemma[jax]) (1.6.0)\n",
"Requirement already satisfied: protobuf in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from orbax-checkpoint==0.5.7->recurrentgemma[jax]) (4.25.2)\n",
"Collecting optax\n",
" Downloading optax-0.2.2-py3-none-any.whl (223 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m223.7/223.7 kB\u001b[0m \u001b[31m29.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: rich>=11.1 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from flax<0.9.0,>=0.8.2->recurrentgemma[jax]) (13.7.1)\n",
"Requirement already satisfied: scipy>=1.9 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from jax<0.5.0,>=0.4.23->recurrentgemma[jax]) (1.12.0)\n",
"Collecting ml-dtypes>=0.2.0\n",
" Downloading ml_dtypes-0.4.0-cp310-cp310-macosx_10_9_universal2.whl (390 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m390.9/390.9 kB\u001b[0m \u001b[31m29.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting opt-einsum\n",
" Downloading opt_einsum-3.3.0-py3-none-any.whl (65 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.5/65.5 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting typeguard==2.13.3\n",
" Downloading typeguard-2.13.3-py3-none-any.whl (17 kB)\n",
"Requirement already satisfied: markdown-it-py>=2.2.0 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from rich>=11.1->flax<0.9.0,>=0.8.2->recurrentgemma[jax]) (3.0.0)\n",
"Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from rich>=11.1->flax<0.9.0,>=0.8.2->recurrentgemma[jax]) (2.17.2)\n",
"Collecting zipp\n",
" Downloading zipp-3.18.1-py3-none-any.whl (8.2 kB)\n",
"Requirement already satisfied: fsspec in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from etils[epath,epy]->orbax-checkpoint==0.5.7->recurrentgemma[jax]) (2023.10.0)\n",
"Requirement already satisfied: importlib_resources in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from etils[epath,epy]->orbax-checkpoint==0.5.7->recurrentgemma[jax]) (6.1.2)\n",
"Collecting chex>=0.1.86\n",
" Downloading chex-0.1.86-py3-none-any.whl (98 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.2/98.2 kB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: toolz>=0.9.0 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from chex>=0.1.86->optax->flax<0.9.0,>=0.8.2->recurrentgemma[jax]) (0.12.1)\n",
"Requirement already satisfied: mdurl~=0.1 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich>=11.1->flax<0.9.0,>=0.8.2->recurrentgemma[jax]) (0.1.2)\n",
"Building wheels for collected packages: recurrentgemma\n",
" Building wheel for recurrentgemma (pyproject.toml) ... \u001b[?25ldone\n",
"\u001b[?25h Created wheel for recurrentgemma: filename=recurrentgemma-0.1.0-py3-none-any.whl size=73483 sha256=fb0155d9d3fe031716dcb26e7c11b10a02f545879b13d6f5286eb200ec90cd86\n",
" Stored in directory: /private/var/folders/jx/gld2clwj7sd_q8hd2m6hztcr0000gn/T/pip-ephem-wheel-cache-62nk7qne/wheels/31/37/18/c57f1df6091b661385ab728b959bdfbf2078d9fc7c856899e4\n",
"Successfully built recurrentgemma\n",
"Installing collected packages: sentencepiece, zipp, typeguard, opt-einsum, msgpack, ml-dtypes, etils, absl-py, tensorstore, jaxtyping, jaxlib, jax, recurrentgemma, chex, orbax-checkpoint, optax, flax\n",
" Attempting uninstall: sentencepiece\n",
" Found existing installation: sentencepiece 0.1.99\n",
" Uninstalling sentencepiece-0.1.99:\n",
" Successfully uninstalled sentencepiece-0.1.99\n",
" Attempting uninstall: absl-py\n",
" Found existing installation: absl-py 2.1.0\n",
" Uninstalling absl-py-2.1.0:\n",
" Successfully uninstalled absl-py-2.1.0\n",
"Successfully installed absl-py-1.4.0 chex-0.1.86 etils-1.7.0 flax-0.8.2 jax-0.4.26 jaxlib-0.4.26 jaxtyping-0.2.28 ml-dtypes-0.4.0 msgpack-1.0.8 opt-einsum-3.3.0 optax-0.2.2 orbax-checkpoint-0.5.7 recurrentgemma-0.1.0 sentencepiece-0.2.0 tensorstore-0.1.56 typeguard-2.13.3 zipp-3.18.1\n",
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
"\u001b[31mERROR: Could not find a version that satisfies the requirement tensorflow-cpu (from versions: none)\u001b[0m\u001b[31m\n",
"\u001b[0m\u001b[31mERROR: No matching distribution found for tensorflow-cpu\u001b[0m\u001b[31m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
"\u001b[31mERROR: Can not perform a '--user' install. User site-packages are not visible in this virtualenv.\u001b[0m\u001b[31m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: datasets in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (2.16.1)\n",
"Requirement already satisfied: pyarrow-hotfix in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from datasets) (0.6)\n",
"Requirement already satisfied: xxhash in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from datasets) (3.4.1)\n",
"Requirement already satisfied: requests>=2.19.0 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from datasets) (2.31.0)\n",
"Requirement already satisfied: fsspec[http]<=2023.10.0,>=2023.1.0 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from datasets) (2023.10.0)\n",
"Requirement already satisfied: numpy>=1.17 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from datasets) (1.24.4)\n",
"Requirement already satisfied: pandas in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from datasets) (2.2.0)\n",
"Requirement already satisfied: multiprocess in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from datasets) (0.70.15)\n",
"Requirement already satisfied: packaging in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from datasets) (23.2)\n",
"Requirement already satisfied: pyyaml>=5.1 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from datasets) (6.0.1)\n",
"Requirement already satisfied: huggingface-hub>=0.19.4 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from datasets) (0.20.3)\n",
"Requirement already satisfied: filelock in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from datasets) (3.13.1)\n",
"Requirement already satisfied: tqdm>=4.62.1 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from datasets) (4.66.1)\n",
"Requirement already satisfied: pyarrow>=8.0.0 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from datasets) (15.0.0)\n",
"Requirement already satisfied: dill<0.3.8,>=0.3.0 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from datasets) (0.3.7)\n",
"Requirement already satisfied: aiohttp in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from datasets) (3.9.1)\n",
"Requirement already satisfied: attrs>=17.3.0 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from aiohttp->datasets) (23.2.0)\n",
"Requirement already satisfied: aiosignal>=1.1.2 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from aiohttp->datasets) (1.3.1)\n",
"Requirement already satisfied: frozenlist>=1.1.1 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from aiohttp->datasets) (1.4.1)\n",
"Requirement already satisfied: yarl<2.0,>=1.0 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from aiohttp->datasets) (1.9.4)\n",
"Requirement already satisfied: multidict<7.0,>=4.5 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from aiohttp->datasets) (6.0.4)\n",
"Requirement already satisfied: async-timeout<5.0,>=4.0 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from aiohttp->datasets) (4.0.3)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from huggingface-hub>=0.19.4->datasets) (4.9.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from requests>=2.19.0->datasets) (2023.11.17)\n",
"Requirement already satisfied: idna<4,>=2.5 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from requests>=2.19.0->datasets) (3.6)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from requests>=2.19.0->datasets) (3.3.2)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from requests>=2.19.0->datasets) (2.2.0)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from pandas->datasets) (2.8.2)\n",
"Requirement already satisfied: tzdata>=2022.7 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from pandas->datasets) (2023.4)\n",
"Requirement already satisfied: pytz>=2020.1 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from pandas->datasets) (2023.4)\n",
"Requirement already satisfied: six>=1.5 in /Users/tybalex/.pyenv/versions/3.10.12/envs/new3102/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
]
}
],
"source": [
"# @title Installation\n",
"! pip install 'git+https://github.com/google-deepmind/recurrentgemma.git#egg=recurrentgemma[jax]'\n",
"! pip install tensorflow-cpu # Might require a session restart\n",
"! pip install --user kaggle\n",
"! pip install datasets"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "yWaP_LPoEcoY"
},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'tensorflow'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[10], line 20\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrecurrentgemma\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m jax \u001b[38;5;28;01mas\u001b[39;00m recurrentgemma\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# We will use tensorflow to handle the dataset\u001b[39;00m\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow_datasets\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtfds\u001b[39;00m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
]
}
],
"source": [
"# @title Python imports\n",
"import pathlib\n",
"from typing import Any, Mapping, Iterator\n",
"import enum\n",
"import functools\n",
"\n",
"# We import JAX and some related packages.\n",
"import chex\n",
"import jax\n",
"import jax.numpy as jnp\n",
"import optax\n",
"\n",
"\n",
"\n",
"# Finally, we import Recurrentgemma.\n",
"import sentencepiece as spm\n",
"from recurrentgemma import jax as recurrentgemma\n",
"\n",
"# We will use tensorflow to handle the dataset\n",
"import tensorflow as tf\n",
"import tensorflow_datasets as tfds"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "iLafhtv3Rg5F"
},
"source": [
"### Downloading the checkpoint\n",
"\n",
"To use Griffin's checkpoints, you'll need a Kaggle account and API key. Here's how to get them:\n",
"\n",
"1. Visit https://www.kaggle.com/ and create an account.\n",
"2. Go to your account settings, then the 'API' section.\n",
"3. Click 'Create new token' to download your key.\n",
"\n",
"You will also need to acknowledge the Terms and Conditions of the RecrurrentGemma models on https://www.kaggle.com/models/google/recurrentgemma/ in order to be able to download the model weights and the tokenizer.\n",
"\n",
"Then run the cell below."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jCZSmEVDVv6O"
},
"source": [
"If everything went well, you should see:\n",
"```\n",
"Kaggle credentials set.\n",
"Kaggle credentials successfully validated.\n",
"```\n",
"\n",
"Now select and download the checkpoint you want to try. The 2b model can fit in memory for fine-tuning."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DVgmx04E2ztl"
},
"source": [
"Need to visit the kaggle page and agree to their term."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "RoUb7Shg-bex"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"fatal: destination path 'recurrentg-2b-it' already exists and is not an empty directory.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/tybalex/.pyenv/versions/3.10.12/lib/python3.10/pty.py:89: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
" pid, fd = os.forkpty()\n"
]
}
],
"source": [
"!git clone https://huggingface.co/yingbei/recurrentg-2b-it\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"id": "1TOdNwcNBhno"
},
"outputs": [],
"source": [
"VARIANT = '2b-it' # @param ['2b', '2b-it'] {type:\"string\"}\n",
"weights_dir = pathlib.Path(\"./recurrentg-2b-it\")\n",
"ckpt_path = weights_dir / VARIANT\n",
"vocab_path = weights_dir / 'tokenizer.model'"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ejQhgtjbEcoY"
},
"source": [
"## Step 1: prepare the dataset\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "XeynYJXCEymJ"
},
"outputs": [],
"source": [
"from datasets import load_dataset\n",
"code_sharegpt = load_dataset(\"sanjay920/code74k-sharegpt\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yDhp3v7DFSUd"
},
"outputs": [],
"source": [
"code_sharegpt[\"train\"][0][\"conversations\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jOMGn19rG5JE"
},
"outputs": [],
"source": [
"import json\n",
"chat_prefix = \"<start_of_turn>\"\n",
"chat_suffix = \"<end_of_turn>\"\n",
"user_role = \"user\\n\"\n",
"preprocessed_code_sharegpt_data = []\n",
"for itor in code_sharegpt[\"train\"]:\n",
" c = itor[\"conversations\"]\n",
" c = json.loads(c)\n",
" assert c[-1][\"from\"] == \"gpt\"\n",
" assert c[0][\"from\"] == \"human\"\n",
" assert len(c) == 2\n",
" input = chat_prefix + user_role + c[0][\"value\"] + chat_suffix\n",
" output = c[1][\"value\"]\n",
" preprocessed_code_sharegpt_data.append({\"input\": input, \"output\": output})\n",
"\n",
"print(json.dumps(preprocessed_code_sharegpt_data[0], indent=4))\n",
"print(len(preprocessed_code_sharegpt_data))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "oZSVAbmWVD1q"
},
"outputs": [],
"source": [
"\n",
"def load_custom_data(data):\n",
" # convert list of dicts to tfds dataset format\n",
" def preprocess(item):\n",
" # Convert your item here, e.g., tokenize text\n",
" return {\n",
" 'src': item['input'], # Assume these are already preprocessed\n",
" 'dst': item['output'],\n",
" }\n",
"\n",
" # Create a Dataset from the list of dictionaries\n",
" ds = tf.data.Dataset.from_generator(lambda: (preprocess(item) for item in data),\n",
" output_types={'src': tf.string, 'dst': tf.string})\n",
"\n",
" # Further dataset operations (batching, padding, etc.) go here\n",
" # For example, to batch:\n",
" # ds = ds.batch(2)\n",
"\n",
" return ds"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NYC42hJgEcoY"
},
"source": [
"### Tokenizer\n",
"\n",
"Let's start by loading our vocabulary base tokenizer, which we'll construct using the [SentencePiece](https://github.com/google/sentencepiece) library."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "TpyG5YW1EcoY"
},
"outputs": [],
"source": [
"vocab = spm.SentencePieceProcessor()\n",
"vocab.Load(str(vocab_path))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ab2MSf-qEcoY"
},
"source": [
"Let's customize `SentencePieceProcessor` for our English-to-French translation task. Since we're fine-tuning the English-only Griffin 2B model, we need a few adjustments:\n",
"\n",
"- **Input Prefix**: Adding a common prefix to each input signals the translation task. For example we could go with a prompt like `Translate this into French: [INPUT_SENTENCE]`.\n",
"\n",
"- **Translation Start suffix**: We add a suffix at the end of each prompt tells the model exactly when to begin the translation process. A new line should do the job.\n",
"\n",
"- **LM Tokens**: Griffin models expect a *beginning of sequence* token at the beginning of each sequence. Similarly, we need to add an *end of sequence* token at the end of each training example."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "L9cjK0uxEcoY"
},
"outputs": [],
"source": [
"class GriffinTokenizer:\n",
" \"\"\"Custom wrapper around a SentencePieceProcessor for tensorflow.\"\"\"\n",
"\n",
" def __init__(self, spm_processor: spm.SentencePieceProcessor):\n",
" self._spm_processor = spm_processor\n",
"\n",
" @property\n",
" def pad_id(self) -> int:\n",
" \"\"\"Fast access to the pad id.\"\"\"\n",
" return self._spm_processor.pad_id()\n",
"\n",
" def tokenize(\n",
" self,\n",
" example: str | bytes,\n",
" prefix: str = '',\n",
" suffix: str = '',\n",
" add_eos: bool = True,\n",
" ) -> jax.Array:\n",
" \"\"\"\n",
" Tokenization function.\n",
"\n",
" Args:\n",
" example: input string to tokenize.\n",
" prefix: prefix to add to the input string.\n",
" suffix: suffix to add to the input string.\n",
" add_eos: if True, add an end of sentence token at the end of the output\n",
" sequence.\n",
" Returns:\n",
" Tokens corresponding to the input string.\n",
" \"\"\"\n",
" int_list = [self._spm_processor.bos_id()]\n",
" int_list.extend(self._spm_processor.EncodeAsIds(prefix + example + suffix))\n",
" if add_eos:\n",
" int_list.append(self._spm_processor.eos_id())\n",
"\n",
" return jnp.array(int_list, dtype=jnp.int32)\n",
"\n",
" def tokenize_tf_op(\n",
" self,\n",
" str_tensor: tf.Tensor,\n",
" prefix: str = '',\n",
" suffix: str = '',\n",
" add_eos: bool = True,\n",
" ) -> tf.Tensor:\n",
" \"\"\"Tensforflow operator for the tokenize function.\"\"\"\n",
" encoded = tf.numpy_function(\n",
" self.tokenize,\n",
" [str_tensor, prefix, suffix, add_eos],\n",
" tf.int32)\n",
" encoded.set_shape([None])\n",
" return encoded\n",
"\n",
" def to_string(self, tokens: jax.Array) -> str:\n",
" \"\"\"Convert an array of tokens to a string.\"\"\"\n",
" return self._spm_processor.EncodeIds(tokens.tolist())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6xuCVkurEcoY"
},
"source": [
"Now let's try our custom tokenizer on the MTNT dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "xEA-97ioEcoY"
},
"outputs": [],
"source": [
"def tokenize_source(tokenizer, example: tf.Tensor):\n",
" return tokenizer.tokenize_tf_op(\n",
" example,\n",
" prefix='',\n",
" suffix='\\n<start_of_turn>model\\n',\n",
" add_eos=False\n",
" )\n",
"def tokenize_destination(tokenizer, example: tf.Tensor):\n",
" return tokenizer.tokenize_tf_op(example, add_eos=True)\n",
"\n",
"tokenizer = GriffinTokenizer(vocab)\n",
"# ds = tfds.load(\"mtnt/en-fr\",split=\"train\")\n",
"\n",
"# ds = ds.take(2)\n",
"# for d in ds:\n",
"# print(d)\n",
"\n",
"ds = load_custom_data(preprocessed_code_sharegpt_data[:2])\n",
"print(ds)\n",
"ds = ds.map(lambda x: {\n",
" 'input': tokenize_source(tokenizer, x['src']),\n",
" 'output': tokenize_destination(tokenizer, x['dst'])\n",
" })\n",
"ds = ds.as_numpy_iterator()\n",
"for idx, example in enumerate(ds):\n",
" print(f'Example {idx}:')\n",
" for key, val in example.items():\n",
" print(f'{key}: {val}')\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "r-x0aTugEcoY"
},
"source": [
"### Data loader\n",
"\n",
"We can now wrap everything a build our data loader."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "XwFFs2mDEcoY"
},
"outputs": [],
"source": [
"@chex.dataclass(frozen=True)\n",
"class TrainingInput:\n",
" # Input tokens given to the model\n",
" input_tokens: jax.Array\n",
"\n",
" # A mask that determines which tokens contribute to the target loss\n",
" # calculation.\n",
" target_mask: jax.Array\n",
"\n",
"class DatasetSplit(enum.Enum):\n",
" TRAIN = 'train'\n",
" VALIDATION = 'valid'\n",
"\n",
"\n",
"class MyDatasetBuilder:\n",
" \"\"\"Data loader for the MTNT dataset.\"\"\"\n",
"\n",
" N_ITEMS = {DatasetSplit.TRAIN: 2000, DatasetSplit.VALIDATION: 100}\n",
"\n",
" BUFFER_SIZE_SHUFFLE = 1000\n",
" TRANSLATION_PREFIX = ''\n",
" TRANSLATION_SUFFIX = '\\n<start_of_turn>model\\n'\n",
"\n",
" def __init__(self,\n",
" tokenizer : GriffinTokenizer,\n",
" max_seq_len: int):\n",
" \"\"\"Constructor.\n",
"\n",
" Args:\n",
" tokenizer: Gemma tokenizer to use.\n",
" max_seq_len: size of each sequence in a given batch.\n",
" \"\"\"\n",
" self._tokenizer = tokenizer\n",
" self._base_data = {\n",
" DatasetSplit.TRAIN: load_custom_data(preprocessed_code_sharegpt_data[:2000]),\n",
" DatasetSplit.VALIDATION: load_custom_data(preprocessed_code_sharegpt_data[-100:]),\n",
" }\n",
" self._max_seq_len = max_seq_len\n",
"\n",
" def _tokenize_source(self, example: tf.Tensor):\n",
" \"\"\"Tokenization function for the source.\"\"\"\n",
" return self._tokenizer.tokenize_tf_op(\n",
" example, prefix=self.TRANSLATION_PREFIX, suffix=self.TRANSLATION_SUFFIX,\n",
" add_eos=False\n",
" )\n",
"\n",
" def _tokenize_destination(self, example: tf.Tensor):\n",
" \"\"\"Tokenization function for the French translation.\"\"\"\n",
" return self._tokenizer.tokenize_tf_op(example, add_eos=True)\n",
"\n",
" def _pad_up_to_max_len(self,\n",
" input_tensor: tf.Tensor,\n",
" pad_value: int | bool,\n",
" ) -> tf.Tensor:\n",
" \"\"\"Pad the given tensor up to sequence length of a batch.\"\"\"\n",
" seq_len = tf.shape(input_tensor)[0]\n",
" to_pad = tf.maximum(self._max_seq_len - seq_len, 0)\n",
" return tf.pad(\n",
" input_tensor, [[0, to_pad]], mode='CONSTANT', constant_values=pad_value,\n",
" )\n",
"\n",
" def _to_training_input(\n",
" self,\n",
" src_tokens: jax.Array,\n",
" dst_tokens: jax.Array,\n",
" ) -> TrainingInput:\n",
" \"\"\"Build a training input from a tuple of source and destination tokens.\"\"\"\n",
"\n",
" # The input sequence fed to the model is simply the concatenation of the\n",
" # source and the destination.\n",
" tokens = tf.concat([src_tokens, dst_tokens], axis=0)\n",
"\n",
" # We want to prevent the model from updating based on the source (input)\n",
" # tokens. To achieve this, we add a target mask to each input.\n",
" q_mask = tf.zeros_like(src_tokens, dtype=tf.bool)\n",
" a_mask = tf.ones_like(dst_tokens, dtype=tf.bool)\n",
" mask = tf.concat([q_mask, a_mask], axis=0)\n",
"\n",
" # If the output tokens sequence is smaller than the target sequence size,\n",
" # then we pad it with pad tokens.\n",
" tokens = self._pad_up_to_max_len(tokens, self._tokenizer.pad_id)\n",
"\n",
" # We don't want to perform the backward on the pad tokens.\n",
" mask = self._pad_up_to_max_len(mask, False)\n",
"\n",
" return TrainingInput(input_tokens=tokens, target_mask=mask)\n",
"\n",
"\n",
" def get_train_dataset(self, batch_size: int, num_epochs: int):\n",
" \"\"\"Build the training dataset.\"\"\"\n",
"\n",
" # Tokenize each sample\n",
" ds = self._base_data[DatasetSplit.TRAIN].map(\n",
" lambda x : (self._tokenize_source(x['src']),\n",
" self._tokenize_destination(x['dst']))\n",
" )\n",
" print(ds)\n",
"\n",
" # Convert them to training inputs\n",
" ds = ds.map(lambda x, y: self._to_training_input(x, y))\n",
"\n",
" # Remove the samples which are too long\n",
" ds = ds.filter(lambda x: tf.shape(x.input_tokens)[0] <= self._max_seq_len)\n",
"\n",
" # Shuffle the dataset\n",
" ds = ds.shuffle(buffer_size=self.BUFFER_SIZE_SHUFFLE)\n",
"\n",
" # Repeat if necessary\n",
" ds = ds.repeat(num_epochs)\n",
"\n",
" # Build batches\n",
" ds = ds.batch(batch_size, drop_remainder=True)\n",
" return ds\n",
"\n",
" def get_validation_dataset(self, batch_size: int):\n",
" \"\"\"Build the validation dataset.\"\"\"\n",
"\n",
" # Same as the training dataset, but no shuffling and no repetition\n",
" ds = self._base_data[DatasetSplit.VALIDATION].map(\n",
" lambda x : (self._tokenize_source(x['src']),\n",
" self._tokenize_destination(x['dst']))\n",
" )\n",
" ds = ds.map(lambda x, y: self._to_training_input(x, y))\n",
" ds = ds.filter(lambda x: tf.shape(x.input_tokens)[0] <= self._max_seq_len)\n",
" ds = ds.batch(batch_size, drop_remainder=True)\n",
" return ds"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "m-BHqBGBVlei"
},
"source": [
"# backup dataset class"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "daHyZFztVkkE"
},
"outputs": [],
"source": [
"class MTNTDatasetBuilder:\n",
" \"\"\"Data loader for the MTNT dataset.\"\"\"\n",
"\n",
" N_ITEMS = {DatasetSplit.TRAIN: 35_692, DatasetSplit.VALIDATION: 811}\n",
"\n",
" BUFFER_SIZE_SHUFFLE = 10_000\n",
" TRANSLATION_PREFIX = 'Translate this into French:\\n'\n",
" TRANSLATION_SUFFIX = '\\n'\n",
"\n",
" def __init__(self,\n",
" tokenizer : GriffinTokenizer,\n",
" max_seq_len: int):\n",
" \"\"\"Constructor.\n",
"\n",
" Args:\n",
" tokenizer: Gemma tokenizer to use.\n",
" max_seq_len: size of each sequence in a given batch.\n",
" \"\"\"\n",
" self._tokenizer = tokenizer\n",
" self._base_data = {\n",
" DatasetSplit.TRAIN: tfds.load(\"mtnt/en-fr\",split=\"train\"),\n",
" DatasetSplit.VALIDATION: tfds.load(\"mtnt/en-fr\",split=\"valid\"),\n",
" }\n",
" self._max_seq_len = max_seq_len\n",
"\n",
" def _tokenize_source(self, example: tf.Tensor):\n",
" \"\"\"Tokenization function for the source.\"\"\"\n",
" return self._tokenizer.tokenize_tf_op(\n",
" example, prefix=self.TRANSLATION_PREFIX, suffix=self.TRANSLATION_SUFFIX,\n",
" add_eos=False\n",
" )\n",
"\n",
" def _tokenize_destination(self, example: tf.Tensor):\n",
" \"\"\"Tokenization function for the French translation.\"\"\"\n",
" return self._tokenizer.tokenize_tf_op(example, add_eos=True)\n",
"\n",
" def _pad_up_to_max_len(self,\n",
" input_tensor: tf.Tensor,\n",
" pad_value: int | bool,\n",
" ) -> tf.Tensor:\n",
" \"\"\"Pad the given tensor up to sequence length of a batch.\"\"\"\n",
" seq_len = tf.shape(input_tensor)[0]\n",
" to_pad = tf.maximum(self._max_seq_len - seq_len, 0)\n",
" return tf.pad(\n",
" input_tensor, [[0, to_pad]], mode='CONSTANT', constant_values=pad_value,\n",
" )\n",
"\n",
" def _to_training_input(\n",
" self,\n",
" src_tokens: jax.Array,\n",
" dst_tokens: jax.Array,\n",
" ) -> TrainingInput:\n",
" \"\"\"Build a training input from a tuple of source and destination tokens.\"\"\"\n",
"\n",
" # The input sequence fed to the model is simply the concatenation of the\n",
" # source and the destination.\n",
" tokens = tf.concat([src_tokens, dst_tokens], axis=0)\n",
"\n",
" # We want to prevent the model from updating based on the source (input)\n",
" # tokens. To achieve this, we add a target mask to each input.\n",
" q_mask = tf.zeros_like(src_tokens, dtype=tf.bool)\n",
" a_mask = tf.ones_like(dst_tokens, dtype=tf.bool)\n",
" mask = tf.concat([q_mask, a_mask], axis=0)\n",
"\n",
" # If the output tokens sequence is smaller than the target sequence size,\n",
" # then we pad it with pad tokens.\n",
" tokens = self._pad_up_to_max_len(tokens, self._tokenizer.pad_id)\n",
"\n",
" # We don't want to perform the backward on the pad tokens.\n",
" mask = self._pad_up_to_max_len(mask, False)\n",
"\n",
" return TrainingInput(input_tokens=tokens, target_mask=mask)\n",
"\n",
"\n",
" def get_train_dataset(self, batch_size: int, num_epochs: int):\n",
" \"\"\"Build the training dataset.\"\"\"\n",
"\n",
" # Tokenize each sample\n",
" ds = self._base_data[DatasetSplit.TRAIN].map(\n",
" lambda x : (self._tokenize_source(x['src']),\n",
" self._tokenize_destination(x['dst']))\n",
" )\n",
"\n",
" # Convert them to training inputs\n",
" ds = ds.map(lambda x, y: self._to_training_input(x, y))\n",
"\n",
" # Remove the samples which are too long\n",
" ds = ds.filter(lambda x: tf.shape(x.input_tokens)[0] <= self._max_seq_len)\n",
"\n",
" # Shuffle the dataset\n",
" ds = ds.shuffle(buffer_size=self.BUFFER_SIZE_SHUFFLE)\n",
"\n",
" # Repeat if necessary\n",
" ds = ds.repeat(num_epochs)\n",
"\n",
" # Build batches\n",
" ds = ds.batch(batch_size, drop_remainder=True)\n",
" return ds\n",
"\n",
" def get_validation_dataset(self, batch_size: int):\n",
" \"\"\"Build the validation dataset.\"\"\"\n",
"\n",
" # Same as the training dataset, but no shuffling and no repetition\n",
" ds = self._base_data[DatasetSplit.VALIDATION].map(\n",
" lambda x : (self._tokenize_source(x['src']),\n",
" self._tokenize_destination(x['dst']))\n",
" )\n",
" ds = ds.map(lambda x, y: self._to_training_input(x, y))\n",
" ds = ds.filter(lambda x: tf.shape(x.input_tokens)[0] <= self._max_seq_len)\n",
" ds = ds.batch(batch_size, drop_remainder=True)\n",
" return ds"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WsOYxL8XXSqf"
},
"source": [
"# Try"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_Sq9uC15EcoZ"
},
"source": [
"Let's give it a try."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "bYeduOaNEcoZ"
},
"outputs": [],
"source": [
"dataset_builder = MyDatasetBuilder(tokenizer, max_seq_len=4000)\n",
"ds = dataset_builder.get_train_dataset(3, 1)\n",
"ds = ds.take(2)\n",
"ds = ds.as_numpy_iterator()\n",
"for idx, example in enumerate(ds):\n",
" print(f'Example {idx}:')\n",
" for key, val in example.items():\n",
" print(f'{key}: {val}')\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_VsT2o6JEcoZ"
},
"source": [
"## Fine tuning Griffin\n",
"\n",
"### Getting started\n",
"\n",
"First let's load the model. Use the `griffin_lib.GriffinConfig.from_flax_params_or_variables` function to automatically load the correct configuration from a checkpoint."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "VDlfziQVEcoZ"
},
"outputs": [],
"source": [
"# Load parameters\n",
"params = recurrentgemma.load_parameters(ckpt_path, \"single_device\")\n",
"config = recurrentgemma.GriffinConfig.from_flax_params_or_variables(params)\n",
"model = recurrentgemma.Griffin(config)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cGbfx6XVEcoZ"
},
"source": [
"Can our model translate French ? Well let's try it out !"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "jWr6Sea_EcoZ"
},
"outputs": [],
"source": [
"sampler = recurrentgemma.Sampler(model=model, vocab=vocab, params=params)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "S6937NTjEcoZ"
},
"outputs": [],
"source": [
"output = sampler(\n",
" [\"Develop a Python code snippet that generates an abbreviated version of a given full name.\\nname = 'John Smith'\"],\n",
" # number of steps performed when generating\n",
" total_generation_steps=300,\n",
")\n",
"print(output.text[0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0Z0CXW4REcoZ"
},
"source": [
"As expected, it didn't work. Let's see if we can get better results by fine-tuning."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gxf6gVGCEcoZ"
},
"source": [
"### Model forward and loss function\n",
"\n",
"The `Griffin` class inherits from [`flax.linen.Module`](https://flax.readthedocs.io/en/latest/guides/flax_fundamentals/flax_basics.html). It offers two essential methods:\n",
"\n",
"- `init`: Initializes the model's parameters.\n",
"\n",
"- `apply`: Executes the model's `__call__` function using a given set of parameters.\n",
"\n",
"Since are working with pre-trained weights, we won't use the `init` function.\n",
"\n",
"With it we can now build the `forward_function` which performs the forward pass and loss computation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "iEcV0XEEEcoZ"
},
"outputs": [],
"source": [
"def forward_and_loss_fn(\n",
" params,\n",
" *,\n",
" model: recurrentgemma.Griffin,\n",
" input_tokens: jax.Array, # Shape [B, L]\n",
" input_mask: jax.Array, # Shape [B, L]\n",
" positions: jax.Array, # Shape [B, L]\n",
") -> jax.Array:\n",
" \"\"\"Forward pass and loss function.\n",
"\n",
" Args:\n",
" params: model's input parameters.\n",
" model: Griffin model to call.\n",
" input_tokens: input tokens sequence, shape [B, L].\n",
" input_mask: tokens to ignore when computing the loss, shape [B, L].\n",
" positions: relative position of each token, shape [B, L].\n",
"\n",
" Returns:\n",
" Softmax cross-entropy loss for the next-token prediction task.\n",
" \"\"\"\n",
" batch_size = input_tokens.shape[0]\n",
" # Foward pass on the input data.\n",
" # No attention cache is needed here.\n",
" # Exclude the last step as it does not appear in the targets.\n",
" logits, _ = model.apply(\n",
" {\"params\": params},\n",
" tokens=input_tokens[:, :-1],\n",
" segment_pos=positions[:, :-1],\n",
" cache=None,\n",
" )\n",
"\n",
" # Similarly, the first token cannot be predicteds.\n",
" target_tokens = input_tokens[:, 1:]\n",
" target_mask = input_mask[:, 1:]\n",
"\n",
" # Convert the target labels into one-hot encoded vectors.\n",
" one_hot = jax.nn.one_hot(target_tokens, logits.shape[-1])\n",
"\n",
" # Don't update on unwanted tokens.\n",
" one_hot = one_hot * target_mask.astype(one_hot.dtype)[...,None]\n",
"\n",
" # Normalisation factor.\n",
" norm_factor = batch_size * (jnp.sum(target_mask) + 1e-8)\n",
"\n",
" # Return the nll loss.\n",
" return -jnp.sum(jax.nn.log_softmax(logits) * one_hot) / norm_factor"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xbxYMMWLEcoZ"
},
"source": [
"We can now build the train_step function which performs the backward pass and updates the model's parameters accordingly."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "cPSfp7ZUEcoZ"
},
"outputs": [],
"source": [
"Params = Mapping[str, Any]\n",
"\n",
"def get_positions(example: jax.Array, pad_id : int) -> jax.Array:\n",
" \"\"\"Builds the position vector from the given tokens.\"\"\"\n",
" pad_mask = example != pad_id\n",
" positions = jnp.cumsum(pad_mask, axis=-1)\n",
" # Subtract one for all positions from the first valid one as they are\n",
" # 0-indexed\n",
" positions = positions - (positions >= 1)\n",
" return positions\n",
"\n",
"@functools.partial(\n",
" jax.jit,\n",
" static_argnames=['model', 'optimizer'],\n",
" donate_argnames=['params', 'opt_state'],\n",
")\n",
"def train_step(\n",
" model: recurrentgemma.Griffin,\n",
" params: Params,\n",
" optimizer: optax.GradientTransformation,\n",
" opt_state: optax.OptState,\n",
" pad_id: int,\n",
" example: TrainingInput,\n",
") -> tuple[jax.Array, Params, optax.OptState]:\n",
" \"\"\"Train step.\n",
"\n",
" Args:\n",
" model: Griffin model.\n",
" params: model's input parameters.\n",
" optimizer: optax optimizer to use.\n",
" opt_state: input optimizer's state.\n",
" pad_id: id of the pad token.\n",
" example: input batch.\n",
"\n",
" Returns:\n",
" Training loss, updated parameters, updated optimizer state.\n",
" \"\"\"\n",
"\n",
" positions = get_positions(example.input_tokens, pad_id)\n",
"\n",
" # Forward and backward passes\n",
" train_loss, grads = jax.value_and_grad(forward_and_loss_fn)(\n",
" params,\n",
" model=model,\n",
" input_tokens=example.input_tokens,\n",
" input_mask=example.target_mask,\n",
" positions=positions,\n",
" )\n",
" # Update the parameters\n",
" updates, opt_state = optimizer.update(grads, opt_state, params)\n",
" params = optax.apply_updates(params, updates)\n",
"\n",
" return train_loss, params, opt_state"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "R2QXp116EcoZ"
},
"source": [
"Similarly, we build a `validation_step` function without backward pass."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "yU4oR92YEcoa"
},
"outputs": [],
"source": [
"@functools.partial(jax.jit, static_argnames=['model'])\n",
"def validation_step(\n",
" model: recurrentgemma.Griffin,\n",
" params: Params,\n",
" pad_id: int,\n",
" example: TrainingInput,\n",
") -> jax.Array:\n",
" return forward_and_loss_fn(\n",
" params,\n",
" model=model,\n",
" input_tokens=example.input_tokens,\n",
" input_mask=example.target_mask,\n",
" positions=get_positions(example.input_tokens, pad_id),\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6g6LFWJbEcoa"
},
"source": [
"And now the training loop itself."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "xT4bAqNLEcoa"
},
"outputs": [],
"source": [
"def train_loop(\n",
" model: recurrentgemma.Griffin,\n",
" params: Params,\n",
" optimizer: optax.GradientTransformation,\n",
" train_ds: Iterator[TrainingInput],\n",
" validation_ds: Iterator[TrainingInput],\n",
" num_steps: int | None = None,\n",
" eval_every_n: int = 20,\n",
"):\n",
" opt_state = jax.jit(optimizer.init)(params)\n",
"\n",
" step_counter = 0\n",
" avg_loss=0\n",
"\n",
" # A first round of validation loss\n",
" n_steps_eval = 0\n",
" eval_loss = 0\n",
" for val_example in validation_ds.as_numpy_iterator():\n",
" eval_loss += validation_step(\n",
" model, params, dataset_builder._tokenizer.pad_id, val_example\n",
" )\n",
" n_steps_eval += 1\n",
" print(f\"Start, validation loss: {eval_loss/n_steps_eval}\")\n",
"\n",
" for train_example in train_ds:\n",
" train_loss, params, opt_state = train_step(\n",
" model=model,\n",
" params=params,\n",
" optimizer=optimizer,\n",
" opt_state=opt_state,\n",
" pad_id=dataset_builder._tokenizer.pad_id,\n",
" example=train_example,\n",
" )\n",
"\n",
" step_counter += 1\n",
" avg_loss += train_loss\n",
" if step_counter % eval_every_n == 0:\n",
" eval_loss = 0\n",
"\n",
" n_steps_eval = 0\n",
" val_iterator = validation_ds.as_numpy_iterator()\n",
" for val_example in val_iterator:\n",
" eval_loss += validation_step(\n",
" model,\n",
" params,\n",
" dataset_builder._tokenizer.pad_id,\n",
" val_example,\n",
" )\n",
" n_steps_eval +=1\n",
" avg_loss /= eval_every_n\n",
" eval_loss /= n_steps_eval\n",
" print(f\"STEP {step_counter} training loss: {avg_loss} - eval loss: {eval_loss}\")\n",
" avg_loss=0\n",
" if num_steps is not None and step_counter > num_steps:\n",
" break\n",
" return params"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hJAuU6P1dGCl"
},
"source": [
"Here you have to choose an optimizer. For devices with smaller memory (like the T4 GPU) we suggest to use SGD as it has a much lower memory footprint. To achieve best finetuning performance we suggest to try Adam-W. We have provided optimal hyper parameters for each optimizer for the particular task in this notebook for the '2b-it' checkpoint."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "oMufclhfc-t4"
},
"outputs": [],
"source": [
"def griffin_weight_decay_mask(params_like: optax.Params) -> Any:\n",
" # Don't put weight decay on the RGLRU, the embeddings and any biases\n",
" def enable_weight_decay(path: list[Any], _: Any) -> bool:\n",
" # Parameters in the LRU and embedder\n",
" path = [dict_key.key for dict_key in path]\n",
" if 'rg_lru' in path or 'embedder' in path:\n",
" return False\n",
" # All biases and scales\n",
" if path[-1] in ('b', 'scale'):\n",
" return False\n",
" return True\n",
"\n",
" return jax.tree_util.tree_map_with_path(enable_weight_decay, params_like)\n",
"\n",
"optimizer_choice = \"adamw\" #@param [\"sgd\", \"adamw\"]\n",
"\n",
"if optimizer_choice == \"sgd\":\n",
" optimizer = optax.sgd(learning_rate=1e-3)\n",
" num_steps = 300\n",
"elif optimizer_choice == \"adamw\":\n",
" optimizer = optax.adamw(\n",
" learning_rate=1e-4,\n",
" b2=0.96,\n",
" eps=1e-8,\n",
" weight_decay=0.1,\n",
" mask=griffin_weight_decay_mask,\n",
" )\n",
" num_steps = 100\n",
" pass\n",
"else:\n",
" raise ValueError(f\"Unknown optimizer: {optimizer_choice}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3tSwzfRdfJ_W"
},
"source": [
"Finally we prepare the training and validation datasets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0KFz-9OcfM9-"
},
"outputs": [],
"source": [
"# Small seq size so that everything fits in memory\n",
"num_epochs = 1 #@param {type: \"integer\"}\n",
"batch_size = 1 #@param {type: \"integer\"}\n",
"sequence_length = 4000 #@param {type: \"integer\"}\n",
"\n",
"# Make the dataset builder\n",
"tokenizer = GriffinTokenizer(vocab)\n",
"dataset_builder= MTNTDatasetBuilder(tokenizer, sequence_length + 1)\n",
"\n",
"# Build the training dataset\n",
"train_ds = dataset_builder.get_train_dataset(\n",
" batch_size=batch_size,\n",
" num_epochs=num_epochs,\n",
").as_numpy_iterator()\n",
"\n",
"# Build the validation dataset, with a limited number of samples for this demo\n",
"validation_ds = dataset_builder.get_validation_dataset(\n",
" batch_size=batch_size,\n",
").take(50)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "muwkf_ZgEcoa"
},
"source": [
"We can now fine-tune our model on a limited number of steps."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vyuWnFY5wSlW"
},
"outputs": [],
"source": [
"trained_params = train_loop(\n",
" model=model,\n",
" params=params,\n",
" optimizer=optimizer,\n",
" train_ds=train_ds,\n",
" validation_ds=validation_ds,\n",
" num_steps=num_steps,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "abChlybFEcod"
},
"source": [
"Both the training loss and the validation's are going down. But is it working ?\n",
"\n",
"Let's try again with our previous example. To ensure our input matches the training format, remember to use the prefix 'Translate this into French:\\n' and a newline character at the end. This signals the model to begin translation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "S5F3fk22Ecod"
},
"outputs": [],
"source": [
"sampler.params = trained_params\n",
"output = sampler(\n",
" [\"Translate this into French:\\nHello, my name is Morgane.\\n\"],\n",
" total_generation_steps=30,\n",
")\n",
"print(output.text[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FdSF-xoChOPD"
},
"outputs": [],
"source": []
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [
"iLafhtv3Rg5F",
"m-BHqBGBVlei"
],
"gpuType": "A100",
"private_outputs": true,
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|